AgentPulse: Visual Analytics of Communication and Activity in LLM-Based Multi-Agent Systems

Abstract

The rapid adoption of multi-agent Large Language Model (LLM) architectures introduces new challenges for understanding communication, processing activity, and failures across interacting agents. Recent developer tools and debugging approaches provide workflow construction, conversation inspection, trace navigation, and intervention mechanisms, but the dynamic distribution and propagation of activity across the multi-agent architecture remains difficult to inspect as a coherent visual process. We present AgentPulse, a brain-inspired Visual Analytics (VA) approach that treats inter-agent communication and activity as the primary objects of visual analysis. Functionally related agents are organized into cognitive regions, while animated communication, live cluster activity, an fMRI-inspired agent-level temporal heatmap, semantic anomaly indicators, and a coordinated sequence view expose complementary structural and temporal aspects of an execution. A formative qualitative evaluation with four experts examines the comprehensibility and perceived utility of the proposed visual encodings. Our work contributes a complementary perspective to existing agent debugging interfaces by emphasizing the visual analysis of how activity propagates through and accumulates within multi-agent workflows.